Modelling approaches: the case of schizophrenia

Pharmacoeconomics. 2008;26(8):633-48. doi: 10.2165/00019053-200826080-00002.


Schizophrenia is a chronic disease characterized by periods of relative stability interrupted by acute episodes (or relapses). The course of the disease may vary considerably between patients. Patient histories show considerable inter- and even intra-individual variability. We provide a critical assessment of the advantages and disadvantages of three modelling techniques that have been used in schizophrenia: decision trees, (cohort and micro-simulation) Markov models and discrete event simulation models. These modelling techniques are compared in terms of building time, data requirements, medico-scientific experience, simulation time, clinical representation, and their ability to deal with patient heterogeneity, the timing of events, prior events, patient interaction, interaction between co-variates and variability (first-order uncertainty). We note that, depending on the research question, the optimal modelling approach should be selected based on the expected differences between the comparators, the number of co-variates, the number of patient subgroups, the interactions between co-variates, and simulation time. Finally, it is argued that in case micro-simulation is required for the cost-effectiveness analysis of schizophrenia treatments, a discrete event simulation model is best suited to accurately capture all of the relevant interdependencies in this chronic, highly heterogeneous disease with limited long-term follow-up data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cost-Benefit Analysis / economics*
  • Decision Making*
  • Decision Support Techniques*
  • Humans
  • Markov Chains
  • Models, Economic*
  • Schizophrenia / economics*
  • Schizophrenia / therapy